This invention relates to testing and characterization of microwave of low noise microwave and RF transistors and amplifiers (device under test, DUT); the method disclosed comprises a test setup and a data acquisition and processing method for extracting the “four noise parameters” of said DUT. The test setup uses automatic microwave tuners in order to synthesize reflection factors (or impedances) at the input of said DUT and allow collecting the necessary data using appropriate high sensitivity receivers.
All RF two-ports containing semiconductor devices (DUT) contain internal noise sources which affect the purity of the signal entering at the input port and existing (amplified) at the output port. A common way of characterizing the “purity” of said DUT at each frequency and bias conditions is the “noise figure: NF”. The noise figure is defined as the degradation of the signal to noise ratio between the input and output port of the DUT: NF=(S.in/N.in)/(S.out/N.out) (eq. 1). Since the DUT adds to the transmitted signal its internal noise, the S/N ratio at the input is higher than at the output, therefore NF>1.
It has been established [1] that four real numbers fully describe the noise behavior of any noisy two-port; these are the four noise parameters. By generally accepted convention said four noise parameters (4NP) are: Minimum Noise Figure (NFmin), Equivalent Noise Resistance (Rn) and Optimum Noise Admittance (Yopt=Gopt+j*Bopt) [4]. The noise behavior of a two-port only depends on the admittance of the source and not of the load. The general relationship is: NF(Ys)=NFmin+Rn/Re{Ys}*|Ys−Yopt|2 (eq. 2).
NF(Ys) in eq. (2) being the noise figure of the chain including the DUT and the receiver, the relationship introduced by FRIIS [2] is used to extract the noise figure of the DUT itself: FRIIS' formula is: NF.dut=NF.total−(NF.rec−1)/Gav.dut (eq. 3); hereby NF.dut is the noise figure of the DUT, NF.rec is the noise figure of the receiver and Gav.dut is the available Gain of the DUT for the given frequency and bias conditions. Both NF.rec and Gav.dut depend both, on the S-parameters of the DUT and the source admittance Ys (eq. 2) and [3].
The basic, prior art, test setup is shown in FIG. 1: It comprises a calibrated noise source (1), an impedance tuner (2), a test fixture (3) to hold the DUT and a sensitive noise receiver (4). The tuner (2) and the noise receiver (4) are controlled by a system computer (5), which sets the source admittance Ys (6), created by the tuner, and retrieves digitally the associated noise measurement data from the noise receiver (4). After termination of the measurement session the computer program processes the measured data and extracts the four noise parameters of the DUT for a given frequency and DUT bias conditions. At least 4 values for Ys are required to extract the 4 noise parameters, but in general there have been used between 7 and 11 Ys values, in order to cancel out and compensate for random measurement errors.
From eq. 2 it follows that, in order to determine the four noise parameters, one would have to take four measurements at four different source admittance values Ys. However, noise measurements are extremely sensitive and various disturbances cause measurement errors and uncertainties. It is therefore the accepted procedure to acquire more than four data points, at each frequency and extract the noise parameters using a linearization and error minimization technique [2: Lane]. This method has been used and refined for many years ([5], FIGS. 2, 3 and [6]) but is in general cumbersome and prone to insufficiencies, since the DUT may oscillate or the impedance tuner may create measurement errors, which are difficult to identify and eliminate if there are not enough data points to extract from. The conclusion is that, to improve the reliability of the measurement one needs more data and elaborated extraction algorithms in order to deal with the noise parameter extraction problem as a statistical observation event.
Simpson [5], FIG. 4, discloses a measurement algorithm, which superimposes a tuning loop over a parameter loop; parameter being either frequency or DC bias of the DUT. This is done in order to increase the measurement speed, at the risk of measurement accuracy. In this case the measurement speed is higher, because changing frequency or DC bias is an electronic operation and much faster than changing (mechanical) tuner states. However it is impossible to optimize the tuning pattern for each parameter setting, as Simpson attempts: for each frequency the same tuner probe/slug position corresponds to different source admittance and for each other DC bias point the optimum area of source admittance is different, since the parameters of the DUT change with DC bias. Simpson, however, does not disclose any data cleaning and filtering criteria, beyond an algorithm for optimizing the distribution of tuner settings, which in end-effect is ineffective.
In this invention a fast noise parameter measurement algorithm including a broad family of data structuring and filtering criteria is disclosed, which allows generally valid and effective data point selection and post-measurement processing.